code: def _low_level_step(self, action):
      logger.debug("Low level agent step {}".format(action))
      self.steps_remaining_at_level -= 1
      cur_pos = tuple(self.cur_obs[0])
      goal_pos = self.flat_env._get_new_pos(cur_pos, self.current_goal)
  
      # Step in the actual env
      (
          f_obs,
          f_rew,
          f_terminated,
          f_truncated,
          info,
      ) = self.flat_env.step(action)
      new_pos = tuple(f_obs[0])
      self.cur_obs = f_obs
  
      # Calculate low-level agent observation and reward
      obs = {self.low_level_agent_id: [f_obs, self.current_goal]}
      if new_pos != cur_pos:
          if new_pos == goal_pos:
              rew = {self.low_level_agent_id: 1}
          else:
              rew = {self.low_level_agent_id: -1}
      else:
          rew = {self.low_level_agent_id: 0}
  
      # Handle env termination & transitions back to higher level.
      terminated = {"__all__": False}
      truncated = {"__all__": False}
      if f_terminated or f_truncated:
          terminated["__all__"] = f_terminated
          truncated["__all__"] = f_truncated
          logger.debug("high level final reward {}".format(f_rew))
          rew["high_level_agent"] = f_rew
          obs["high_level_agent"] = f_obs
      elif self.steps_remaining_at_level == 0:
          terminated[self.low_level_agent_id] = True
          truncated[self.low_level_agent_id] = False
          rew["high_level_agent"] = 0
          obs["high_level_agent"] = f_obs
  
      return (
          obs,
          rew,
          terminated,
          truncated,
          {self.low_level_agent_id: info},
      )
  
file_path: ray/rllib/examples/env/windy_maze_env.py
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete, Tuple
  import logging
  import random
  from ray.rllib.env import MultiAgentEnv
label_desc: Use image node_id 5 for calling the HierarchicalWindyMazeEnv obj's underlying member method code with example usage: obj._low_level_step(action) and returns: obs,  rew,  terminated,  truncated,  dict